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Improving the interpretability of GNN predictions through
  conformal-based graph sparsification

Improving the interpretability of GNN predictions through conformal-based graph sparsification

18 April 2024
Pablo Sánchez-Martín
Kinaan Aamir Khan
Isabel Valera
ArXivPDFHTML

Papers citing "Improving the interpretability of GNN predictions through conformal-based graph sparsification"

5 / 5 papers shown
Title
MotifExplainer: a Motif-based Graph Neural Network Explainer
MotifExplainer: a Motif-based Graph Neural Network Explainer
Zhaoning Yu
Hongyang Gao
26
14
0
01 Feb 2022
Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning
  Algorithms
Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning Algorithms
Liyuan Zheng
Tanner Fiez
Zane Alumbaugh
Benjamin J. Chasnov
Lillian J. Ratliff
OffRL
24
38
0
25 Sep 2021
Few-Shot Graph Learning for Molecular Property Prediction
Few-Shot Graph Learning for Molecular Property Prediction
Zhichun Guo
Chuxu Zhang
W. Yu
John E. Herr
Olaf Wiest
Meng-Long Jiang
Nitesh V. Chawla
AI4CE
106
168
0
16 Feb 2021
Towards A Rigorous Science of Interpretable Machine Learning
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez
Been Kim
XAI
FaML
225
3,658
0
28 Feb 2017
Geometric deep learning: going beyond Euclidean data
Geometric deep learning: going beyond Euclidean data
M. Bronstein
Joan Bruna
Yann LeCun
Arthur Szlam
P. Vandergheynst
GNN
231
3,202
0
24 Nov 2016
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